Road traffic sign detection and classification pdf

A new approach namely automatic detection and recognition The recognition process is done by SVM with a bagged kernel which is used for the first time for traffic sign classification.

Experimental results show superior performance in the detection and recognition of road signs. Computational time Variant lighting conditions, occultation and illumination of complexity is also quite low that makes it applicable for the traffic signs are the main reasons for false detection. It is only real time system. It does not have a real time 3. Use Case Diagram approach.

The robotic self- driving car will follow a lane and maintain it. A traffic sign will be installed at several intervals. The car will detect the traffic signs, recognize it and take appropriate actions accordingly.

System Architecture Diagram Figure 3: Use case diagram 3. Phases of the system The entire system is divided into two major phases: Phase I: Lane Detection for self-driving car The lane detection system is the system that will help the car to maintain the lane while running.

This will prevent the car going off-road. The edges of the road will be marked and the path will be computed for the car to move. Although this dataset has some major flaws, these flaws are eliminated by implementing image augmentation. Methodology This section describes the designing process of the entire system. It provides the flow of modules and in-depth description of different phases. Lane Detection A camera is to be mounted on the self-driving car.

The input will be in the form of a video stream which will be in real time. The processing is done at every frame of the video simultaneously. At each frame, an image will be processed and the output will be reflected in no time. Figure 7: Pixel Summation Figure 4: Udacity Self Driving Car Figure 8: Curve detection The five major steps involved in lane detection are: Optimizing the curve: To optimize the curve, the car needs to know the center of the path.

This is done by averaging the Thresholding: It is a type of image segmentation technique histogram values. Thresholding is the process of convertinga color or grayscale image into a binary image which is simply black and white. We use it to partition the image so as to calculate the lane where the car will move.

Figure 9: Optimized curve resulting in center of the lane Figure 5: Thresholding of image 4. Image distortion can be corrected by warping, but it can also be used for artistic purposes e. The same methods can be applied to video as well. Figure 6: Warping of Image having bird's eye view Finding the curve: This is achieved using pixel summation and creating a histogram resulting into the region of interest. Stop ii. No entry iii. Turn right ahead iv. Turn left ahead v. Ahead only vi. Go straight or right vii.

Go straight or left One of the major drawbacks of this dataset is that it is inconsistent. Some classes have a large number of samples and some classes have very few samples.

This will affect the training process of the Traffic Sign Classifier. To overcome this, we have used Image Augmentation. Image Augmentation: We have used image augmentation to Figure Real time detection of lane with curve value expand an existing image data set. It is a powerful technique to artificially create variations in existing databases as the Traffic Sign Classifier data set is very large and inconsistent. Various transformations such as zooming on an existing image, rotating an image by a few degrees, shearing and cropping the existing set of images, etc.

Figure Sample image augmentation 5. Result The Traffic Sign Classifier is This has been done to avoid load on raspberry pi. Also, the most commonly found traffic signs are taken into consideration.

Pawar sir for providing the required resources for the development of the project. I would also like to thank HOD Mrs. Supriya Joshi for suggesting such a great project topic for departmental purposes.

My sincere thanks to my Project Guide Mr. Deore for helping, suggesting new ideas and guiding me throughout the semester. I am also grateful to all the faculty members for their support and encouragement. Hardware limitations: feature and neural network by Md. To have a better M. If the image processing board cannot perform 9 9 convolutions, the masks can be converted into a combination of smaller ones, as shown at the bottom of the next page.

As an example, the mask for a 90 corner can be decom- posed in Sbm1, Sbm3, and Sbm4 masks see Table I. Corner Extraction To obtain a corner of an image, the algorithm follows these steps. This threshold is obtained from an ideal result. Thus, supposing that The mask for the lower right corner type T3 is symmetrical the corner is ideal, the result of the convolution will be with respect to a vertical axis of the mask for the lower maximum for this corner.

The threshold is the necessary left corner. T5, and T6 types , one has to use symmetrical masks with 3 It calculates the center of mass. Although the detection respect to a horizontal axis of the ones used for the warning mask is built to obtain the maximum value of the signs. Then, there are six 9 9 masks for triangular signs. To prove this for the upper left and right corners of the triangular signs.

The center of mass is calculated by is not very large, and, since the grey level of the back- ground is going to be low, the results, which follow, are very 8 similar. This area will also be limited by the maximum and Sbm3 Sbm3 the minimum heights that we expect the sign has in the image.

In this area, a T2-type corner is sought. If it is not found, the algorithm returns to step 1, to continue scanning the image looking for another T1-type corner.

In this second area, a T3-type corner is sought. If it is found, Fig. Points detected as corners. Some of corners that form the triangle and by proving that they are, points on the edge of a sign may appear labeled as corners, in fact, forming an equilateral triangle. The same principle although, in reality, they are not Fig.

The steps for the detection of the triangular signs corner. However, if the figure is observed, there are points are as follows Fig. All the corners that belong type in white, and we can see that the wrong points do not to the types that appear in the sign are sought in the affect the detection, since they appear in areas in which they image, i. The result of applying the algorithm on a real type , and lower right vertex T3 type.

The algorithm forming an equilateral triangle. Triangular sign detection algorithm. If it is not found, the algorithm returns to step c.

The result of applying the algorithm to a real image Fig. Types 2 and 3 corner detection. All the corners that belong to to be used.

From the equations obtained when the optimal the types that appear in the sign are sought in the image, corner detector was described, we can see that, actually, they i. Masks to locate some portions of a circumference C4 type. The algorithm found from the convolution. However, the number of masks used for the study of the corner position is similar to the would be very high for several radii of the circumference.

However, it is possible to use approximate masks that serve a The image is scanned until a C1-type corner is found. The masks built for the b From the C1 corner found, a search area is defined 90 corners are an approximation of small-circumference through two lines that start from it and have slopes arcs located in the 45 , , , and angles.

The of 85 and 95 , respectively. This area will also be positive part of the masks remains within the circle, while limited by the maximum and the minimum heights the negative part coincides with the background.

Therefore, that we expect the sign has in the image. In this the resulting values of the convolution are high Fig. The area, a C2 corner is sought. If it is not found, the main advantage of using these masks is that there is no need for algorithm returns to step a , to continue scanning the new convolutions to detect the circles, since they have already image, looking for another C1 corner.

Since c A second search zone is created, delimited by two the first steps of the algorithm for rectangular signs cannot lines that start from the corner of the C2 corner and differentiate among the points that are, indeed, corners and have slopes of 5 and 5 , respectively, and again, those which belong to a circle, a new step is in charge of by the maximum and the minimum heights that we that. To make the difference between a rectangle and a circle expect the sign has in the image.

In this second in the last stage of the algorithm, three of the four points area, a C3-type corner is sought. If it is found, the collected algorithm considers that the three corners found in in the previous steps are taken, and the circumference that the successive stages correspond to the same sign, passes by them is calculated.

If most points belong to the otherwise it returns to step b. Rectangular sign detection algorithm. An extrapolation of the method is possible, to suppose from two detected corners where the other one or two should be and to pass to the classification step, but is undergoing research. Two options could be taken, either to obtain some features from the inner part of the sign and present them as input patterns or to present the image as the input pattern. The latter was the chosen solution.

Two neural networks were trained because the detection algorithm is different according to the form of the sign, i. The chosen net was a multilayer perceptron. The size of the input layer corresponds to an image of 30 30 pixels, and the output layer is ten, i. The studied nets were three, the number and dimension of their hidden layers being different.

Circumference detection masks. Image Normalization otherwise, it is taken as a rectangular one Fig. The result The first step is to normalize the image obtained by the of applying the algorithm to a real image can be observed in detection module to the dimensions 30 To do this, the Fig. Real signs detection. Training Patterns 4 After making a decision about the net dimensions, a new Nine ideal signs were chosen for the net training Fig. Then, from the chosen ideal patterns, training patterns were obtained.

From every one of the nine signs, another five C. Results were obtained by covering that draft range. In order to compare the results, inner part of the sign. In this way, the system is adapted some test images were chosen, as shown in Fig.

The best to various lighting conditions that the real images will results corresponded to the third network and are shown in present. Table III 0 minimum value, maximum. Ideal signs and test images. The output for the images shown in Fig. The speed of the detection phase is ms for a image. The implementation of the ing research, and the expected speed is between 30—40 ms. Traffic signs detected and classified. It has been proved with different signs and conditions.

For the classification, the detected sign was used as the input pattern for a neural network. The multilayer perceptron was chosen. Several networks with different number or layers and nodes were trained and compared. All the algorithms can be achieved in real time with a PC and a pipeline image IV.

Above all, some improvements are the study A method for the perception of traffic signs by image of partial occlusions and the use of other paradigms of neural analysis has been tested successfully. The algorithm has two networks. Gagnon for her help Guidance, I. Masaki, Ed. Berlin, Germany: Springer-Verlag, , pp. Piccioli, E. De Micheli, and M.

Bessere, S. Estable, B. Ulmer, and D. Crissman and C. IEEE Int. Robotics Autonomous Vehicles, , pp. Estable, J. Schick, F. Stein, R. Jhansen, R. Writter, and Y. Intelligent Conf.

Gonzalez and R. Woods, Digital Image Processing, 2nd ed. Kamada and M. Thorpe, Ed. Norwell, MA: Kluwer, , ch. Dreschler and H. Berlin, Germany: a moving car derived from monocular TV-frame sequence of a street Springer-Verlag, IJCAI, , pp. Luo, H. Potlapalli, and D. Shah and R. Vision, Conf. San Diego, CA, Nov.

Kitchen and A. Intelligent Robots [18] O. Zuniga and R. Luo and H. Rangarajan, M. Shah, and D. Neural Networks, Comput. Vision, Graph. He received the Degree in electrical engineering and the Ph. His research interests include intelli- sensor systems and image data processing methods gent autonomous systems, mobile robots, perception for environment perception and mobile robot relocalization. Luis E.